2014 |
Homenda, Wladyslaw; Luckner, Marcin Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. Abstract | Links | BibTeX | Tagi: Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition @inproceedings{Homenda2014a, title = {Pattern recognition with rejection: Application to handwritten digits}, author = {Wladyslaw Homenda and Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7077288}, doi = {10.1109/WICT.2014.7077288}, isbn = {978-1-4799-8115-1}, year = {2014}, date = {2014-12-01}, booktitle = {2014 4th World Congress on Information and Communication Technologies (WICT 2014)}, pages = {326--331}, publisher = {IEEE}, abstract = {The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study.}, keywords = {Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition}, pubstate = {published}, tppubtype = {inproceedings} } The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study. |
Homenda, Wladyslaw; Luckner, Marcin Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. Abstract | Links | BibTeX | Tagi: Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition @inproceedings{Homenda2014ab, title = {Pattern recognition with rejection: Application to handwritten digits}, author = {Wladyslaw Homenda and Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7077288}, doi = {10.1109/WICT.2014.7077288}, isbn = {978-1-4799-8115-1}, year = {2014}, date = {2014-12-01}, booktitle = {2014 4th World Congress on Information and Communication Technologies (WICT 2014)}, pages = {326--331}, publisher = {IEEE}, abstract = {The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study.}, keywords = {Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition}, pubstate = {published}, tppubtype = {inproceedings} } The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study. |
2006 |
Luckner, Marcin Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tagi: Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition @inproceedings{Luckner2006, title = {Recognition of Noised Patterns Using Non-Disruption Learning Set}, author = {Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021499}, doi = {10.1109/ISDA.2006.223}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {557--562}, publisher = {IEEE}, abstract = {In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones}, keywords = {Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition}, pubstate = {published}, tppubtype = {inproceedings} } In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones |
Luckner, Marcin Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tagi: Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition @inproceedings{Luckner2006d, title = {Recognition of Noised Patterns Using Non-Disruption Learning Set}, author = {Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021499}, doi = {10.1109/ISDA.2006.223}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {557--562}, publisher = {IEEE}, abstract = {In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones}, keywords = {Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition}, pubstate = {published}, tppubtype = {inproceedings} } In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones |
Publikacje
2014 |
Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. |
Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. |
2006 |
Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. |
Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. |